Abstract
In this chapter a formulation of Boolean networks is introduced and their topological structure is then investigated. The matrix expression of logic, discussed in previous chapters, is used and, under this framework, the dynamics of a Boolean network equation is converted into an equivalent algebraic form as a standard discrete-time linear system. Analyzing the transition matrix of the linear system, easily computable formulas are obtained to show (a) the number of fixed points, (b) the numbers of cycles of different lengths, (c) the transient period, which is the time for all points to enter the set of attractors, (d) the basin of each attractor. In addition, algorithms are developed to calculate all the fixed points, cycles, transient periods, and basins of attraction of all attractors. This chapter is partly based on Cheng and Qi (IEEE Trans. Automat. Contr. 55(10):2251–2258, 2010).
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References
Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16, 727–734 (2000)
Albert, R., Barabási, A.: Dynamics of complex systems: Scaling laws for the period of Boolean networks. Phys. Rev. Lett. 84(24), 5660–5663 (2000)
Albert, R., Othmer, H.: The topology and signature of the regulatory interactions predict the expression pattern of the segment polarity genes in drosophila melanogaster. J. Theor. Biol. 223(1), 1–18 (2003)
Aldana, M.: Boolean dynamics of networks with scale-free topology. Phys. D: Nonlinear Phenom. 185(1), 45–66 (2003)
Cheng, D., Qi, H.: A linear representation of dynamics of Boolean networks. IEEE Trans. Automat. Contr. 55(10), 2251–2258 (2010)
Clarke, E., Kroening, D., Ouaknine, J., Strichman, O.: Completeness and complexity of bounded model checking. In: Verification, Model Checking, and Abstract Interpretation. Lecture Notes in Computer Science, vol. 2937, pp. 85–96. Springer, Berlin/Heidelberg (2004)
Drossel, B., Mihaljev, T., Greil, F.: Number and length of attractors in a critical Kauffman model with connectivity one. Phys. Rev. Lett. 94(8), 88,701 (2005)
Farrow, C., Heidel, J., Maloney, J., Rogers, J.: Scalar equations for synchronous Boolean networks with biological applications. IEEE Trans. Neural Netw. 15(2), 348–354 (2004)
Goodwin, B.: Temporal Organization in Cells. Academic Press, San Diego (1963)
Harris, S., Sawhill, B., Wuensche, A., Kauffman, S.: A model of transcriptional regulatory networks based on biases in the observed regulation rules. Complexity 7(4), 23–40 (2002)
Heidel, J., Maloney, J., Farrow, C., Rogers, J.: Finding cycles in synchronous Boolean networks with applications to biochemical systems. Int. J. Bifurc. Chaos 13(3), 535–552 (2003)
Huang, S.: Regulation of cellular states in mammalian cells from a genomewide view. In: Collado-Vodes, J., Hofestadt, R. (eds.) Gene Regulation and Metabolism: Post-Genomic Computational Approaches, pp. 181–220. MIT Press, Cambridge (2002)
Huang, S., Ingber, D.: Shape-dependent control of cell growth, differentiation, and apoptosis: Switching between attractors in cell regulatory networks. Exp. Cell Res. 261(1), 91–103 (2000)
Ideker, T., Galitski, T., Hood, L.: A new approach to decoding life: systems biology. Annu. Rev. Genom. Hum. Genet. 2, 343–372 (2001)
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437 (1969)
Kauffman, S.: The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press, London (1993)
Kitano, H.: Systems biology: a brief overview. Science 259, 1662–1664 (2002)
Langmead, C., Jha, S., Clarke, E.: Temporal-logics as query languages for Dynamic Bayesian Networks: Application to D. melanogaster Embryo Development. Tech. rep., School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 (2006)
Li, Z., Zhao, Y., Cheng, D.: Structure of higher order Boolean networks. Preprint (2010)
Mu, Y., Guo, L.: Optimization and identification in a non-equilibrium dynamic game. In: Proc. CDC-CCC’09, pp. 5750–5755 (2009)
Nurse, P.: A long twentieth century of the cell cycle and beyond. Cell 100(1), 71–78 (2000)
Robert, F.: Discrete Iterations: A Metric Study. Springer, Berlin (1986). Translated by J. Rolne
Samuelsson, B., Troein, C.: Superpolynomial growth in the number of attractors in Kauffman networks. Phys. Rev. Lett. 90(9), 98,701 (2003)
Shmulevich, I., Dougherty, E., Kim, S., Zhang, W.: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2), 261–274 (2002)
Waldrop, M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Touchstone, New York (1992)
Zhao, Q.: A remark on ‘Scalar equations for synchronous Boolean networks with biologicapplications’ by C. Farrow, J. Heidel, J. Maloney, and J. Rogers. IEEE Trans. Neural Netw. 16(6), 1715–1716 (2005)
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Cheng, D., Qi, H., Li, Z. (2011). Topological Structure of a Boolean Network. In: Analysis and Control of Boolean Networks. Communications and Control Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-097-7_5
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DOI: https://doi.org/10.1007/978-0-85729-097-7_5
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